Recorded sensor signals of many electronic devices, including implanted medical devices and remote sensors, are analog in nature, but post-recordation processing of the signals is typically performed using digitally based algorithms. Thus, the analog signals must frequently be converted into digital form prior to applying signal processing algorithms. Digital signals provide other advantages relative to analog signals. For example, digital signals are generally more robust to transmit and typically can be more readily stored electronically. Accordingly, many if not most devices that record analog signals also include some type of analog-to-digital (A/D) converter.
Many data acquisition devices, however, are severely constrained in terms of available power and available bandwidth. Therefore, the use of a power-consuming A/D converter can be a further constraint on the performance of such devices. For example, a limitation regarding A/D converters belonging to the widely-used Nyquist-Rate class of converters, which are characterized by a one-to-one correspondence between an output value and a single input value, is their power consumption. Moreover, when scaled to submicron size, high-resolution analog circuits are affected by low-power supply and poor transistor output resistance, the latter effect being due to the well-known body effect. Thus, a continuing challenge for designers is how to decrease power consumption in devices that record analog signals but perform signal processing digitally.
One solution is to utilize Delta-Sigma converters, which relax requirements for analog circuitry at the expense of more complicated digital circuitry. The underlying principle of Delta-Sigma converters is a sacrifice of resolution in amplitude for resolution in time in such a way that the imprecision of analog circuits can be tolerated. Yet the high resolution in time typically requires a high-speed clock, which results in high power consumption and increased complexity in the digital circuitry employed. Moreover, Delta-Sigma converters tend not to be well suited for systems such as implanted neural recording devices given the typical power and die area constraints often imposed on such devices.
The diminished suitability of Delta-Sigma converters stems from certain unique features of signals such as neural action potentials and speech signals. These types of signals generally exhibit non-stationary properties. With respect to speech, there typically are significant lags between the emission of information-carrying signals and information-free signals, reflecting the often frequent, often lengthy pauses between speech utterances during a conversation. There is generally no need for signal sampling during such lags and pauses, yet conventional systems and devices such as the Nyquist-Rate converter nonetheless continue to expend power operating even when the input signal does not contain useful information.
In the neuronal context, an integrate-and-fire (IF) signal representation mechanism entails passing a regulated analog signal through an IF neuron. The information is losslessly encoded into asynchronous pulse trains fired from the neuron according to specific threshold settings. The pulse train is compatible with digital logic circuits for subsequent processes. This coding method has the advantages of low-power consumption and simpler front-end circuitry, but the analog signal typically must be made strictly positive by adding a DC bias. Overall power will be wasted since the signal has to be shifted up by a worst-case offset, which is the most negative signal value possible during operation of the device.
A problem with this approach is that the DC bias tends to continuously produce spikes in the signal even when the original signal is in an idle state during which there is no useful information conveyed by a sensed signal. Additionally, the DC bias results in an average firing rate that is larger than the Nyquist rate. With some modifications to existing architecture, the DC bias can be eliminated by employing two IF neurons that encode positive and negative signals, respectively. Accordingly, the IF neurons do not respond to the signal when its value is zero. However, an additional problem of the IF signal representation is that the peak firing rate is unbounded. The system could spike at rates that are much larger than the minimum firing rate for perfect reconstruction. The extra pulses lead to wasted power consumption, wasted data bandwidth, and further problems in multiplexing the data off-chip. This peak spiking rate can be reduced with the addition of a neural refractory period wherein after a neuron fires it is disabled for a period of time. This results in the peak firing rate being limited by the inverse of the refractory period.